GestureRecognitionToolkit  Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
ClusterTree Class Reference

#include <ClusterTree.h>

Inheritance diagram for ClusterTree:
Tree Clusterer GRTBase MLBase GRTBase Observer< TrainingResult > Observer< TestInstanceResult >

Public Member Functions

 ClusterTree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT, const bool useScaling=false, const Float minRMSErrorPerNode=0.01)
 
 ClusterTree (const ClusterTree &rhs)
 
virtual ~ClusterTree (void)
 
ClusterTreeoperator= (const ClusterTree &rhs)
 
virtual bool deepCopyFrom (const Clusterer *cluster)
 
virtual bool train_ (MatrixFloat &trainingData)
 
virtual bool predict_ (VectorFloat &inputVector)
 
virtual bool clear ()
 
virtual bool print () const
 
virtual bool saveModelToFile (std::fstream &file) const
 
virtual bool loadModelFromFile (std::fstream &file)
 
ClusterTreeNodedeepCopyTree () const
 
const ClusterTreeNodegetTree () const
 
UINT getPredictedClusterLabel () const
 
Float getMinRMSErrorPerNode () const
 
bool setMinRMSErrorPerNode (const Float minRMSErrorPerNode)
 
- Public Member Functions inherited from Tree
 Tree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT)
 
virtual ~Tree (void)
 
virtual bool getModel (std::ostream &stream) const
 
const NodegetTree () const
 
UINT getTrainingMode () const
 
UINT getNumSplittingSteps () const
 
UINT getMinNumSamplesPerNode () const
 
UINT getMaxDepth () const
 
UINT getPredictedNodeID () const
 
bool getRemoveFeaturesAtEachSpilt () const
 
bool setTrainingMode (const UINT trainingMode)
 
bool setNumSplittingSteps (const UINT numSplittingSteps)
 
bool setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)
 
bool setMaxDepth (const UINT maxDepth)
 
bool setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)
 
- Public Member Functions inherited from GRTBase
 GRTBase (void)
 
virtual ~GRTBase (void)
 
bool copyGRTBaseVariables (const GRTBase *GRTBase)
 
std::string getClassType () const
 
std::string getLastWarningMessage () const
 
std::string getLastErrorMessage () const
 
std::string getLastInfoMessage () const
 
bool setInfoLoggingEnabled (const bool loggingEnabled)
 
bool setWarningLoggingEnabled (const bool loggingEnabled)
 
bool setErrorLoggingEnabled (const bool loggingEnabled)
 
GRTBasegetGRTBasePointer ()
 
const GRTBasegetGRTBasePointer () const
 
- Public Member Functions inherited from Clusterer
 Clusterer (void)
 
virtual ~Clusterer (void)
 
bool copyBaseVariables (const Clusterer *clusterer)
 
virtual bool train_ (ClassificationData &trainingData)
 
virtual bool train_ (UnlabelledData &trainingData)
 
virtual bool reset ()
 
bool getConverged () const
 
UINT getNumClusters () const
 
UINT getPredictedClusterLabel () const
 
Float getMaximumLikelihood () const
 
Float getBestDistance () const
 
VectorFloat getClusterLikelihoods () const
 
VectorFloat getClusterDistances () const
 
Vector< UINT > getClusterLabels () const
 
std::string getClustererType () const
 
bool setNumClusters (const UINT numClusters)
 
ClusterercreateNewInstance () const
 
ClustererdeepCopy () const
 
const ClusterergetBaseClusterer () const
 
- Public Member Functions inherited from MLBase
 MLBase (void)
 
virtual ~MLBase (void)
 
bool copyMLBaseVariables (const MLBase *mlBase)
 
virtual bool train (ClassificationData trainingData)
 
virtual bool train (RegressionData trainingData)
 
virtual bool train_ (RegressionData &trainingData)
 
virtual bool train (TimeSeriesClassificationData trainingData)
 
virtual bool train_ (TimeSeriesClassificationData &trainingData)
 
virtual bool train (ClassificationDataStream trainingData)
 
virtual bool train_ (ClassificationDataStream &trainingData)
 
virtual bool train (UnlabelledData trainingData)
 
virtual bool train (MatrixFloat data)
 
virtual bool predict (VectorFloat inputVector)
 
virtual bool predict (MatrixFloat inputMatrix)
 
virtual bool predict_ (MatrixFloat &inputMatrix)
 
virtual bool map (VectorFloat inputVector)
 
virtual bool map_ (VectorFloat &inputVector)
 
virtual bool save (const std::string filename) const
 
virtual bool load (const std::string filename)
 
virtual bool saveModelToFile (std::string filename) const
 
virtual bool loadModelFromFile (std::string filename)
 
virtual bool getModel (std::ostream &stream) const
 
Float scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
 
virtual std::string getModelAsString () const
 
DataType getInputType () const
 
DataType getOutputType () const
 
UINT getBaseType () const
 
UINT getNumInputFeatures () const
 
UINT getNumInputDimensions () const
 
UINT getNumOutputDimensions () const
 
UINT getMinNumEpochs () const
 
UINT getMaxNumEpochs () const
 
UINT getValidationSetSize () const
 
UINT getNumTrainingIterationsToConverge () const
 
Float getMinChange () const
 
Float getLearningRate () const
 
Float getRootMeanSquaredTrainingError () const
 
Float getTotalSquaredTrainingError () const
 
Float getValidationSetAccuracy () const
 
VectorFloat getValidationSetPrecision () const
 
VectorFloat getValidationSetRecall () const
 
bool getUseValidationSet () const
 
bool getRandomiseTrainingOrder () const
 
bool getTrained () const
 
bool getModelTrained () const
 
bool getScalingEnabled () const
 
bool getIsBaseTypeClassifier () const
 
bool getIsBaseTypeRegressifier () const
 
bool getIsBaseTypeClusterer () const
 
bool enableScaling (const bool useScaling)
 
bool setMaxNumEpochs (const UINT maxNumEpochs)
 
bool setMinNumEpochs (const UINT minNumEpochs)
 
bool setMinChange (const Float minChange)
 
bool setLearningRate (const Float learningRate)
 
bool setUseValidationSet (const bool useValidationSet)
 
bool setValidationSetSize (const UINT validationSetSize)
 
bool setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
 
bool setTrainingLoggingEnabled (const bool loggingEnabled)
 
bool registerTrainingResultsObserver (Observer< TrainingResult > &observer)
 
bool registerTestResultsObserver (Observer< TestInstanceResult > &observer)
 
bool removeTrainingResultsObserver (const Observer< TrainingResult > &observer)
 
bool removeTestResultsObserver (const Observer< TestInstanceResult > &observer)
 
bool removeAllTrainingObservers ()
 
bool removeAllTestObservers ()
 
bool notifyTrainingResultsObservers (const TrainingResult &data)
 
bool notifyTestResultsObservers (const TestInstanceResult &data)
 
MLBasegetMLBasePointer ()
 
const MLBasegetMLBasePointer () const
 
Vector< TrainingResult > getTrainingResults () const
 
- Public Member Functions inherited from Observer< TrainingResult >
virtual void notify (const TrainingResult &data)
 
- Public Member Functions inherited from Observer< TestInstanceResult >
virtual void notify (const TestInstanceResult &data)
 

Protected Member Functions

ClusterTreeNodebuildTree (const MatrixFloat &trainingData, ClusterTreeNode *parent, Vector< UINT > features, UINT &clusterLabel, UINT nodeID)
 
bool computeBestSpilt (const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError)
 
bool computeBestSpiltBestIterativeSpilt (const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError)
 
bool computeBestSpiltBestRandomSpilt (const MatrixFloat &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError)
 
- Protected Member Functions inherited from GRTBase
Float SQR (const Float &x) const
 
- Protected Member Functions inherited from Clusterer
bool saveClustererSettingsToFile (std::fstream &file) const
 
bool loadClustererSettingsFromFile (std::fstream &file)
 
- Protected Member Functions inherited from MLBase
bool saveBaseSettingsToFile (std::fstream &file) const
 
bool loadBaseSettingsFromFile (std::fstream &file)
 

Protected Attributes

Float minRMSErrorPerNode
 
- Protected Attributes inherited from Tree
UINT trainingMode
 
UINT numSplittingSteps
 
UINT minNumSamplesPerNode
 
UINT maxDepth
 
bool removeFeaturesAtEachSpilt
 
Nodetree
 
- Protected Attributes inherited from GRTBase
std::string classType
 
DebugLog debugLog
 
ErrorLog errorLog
 
InfoLog infoLog
 
TrainingLog trainingLog
 
TestingLog testingLog
 
WarningLog warningLog
 
- Protected Attributes inherited from Clusterer
std::string clustererType
 
UINT numClusters
 Number of clusters in the model.
 
UINT predictedClusterLabel
 Stores the predicted cluster label from the most recent predict( )
 
Float maxLikelihood
 
Float bestDistance
 
VectorFloat clusterLikelihoods
 
VectorFloat clusterDistances
 
Vector< UINT > clusterLabels
 
bool converged
 
Vector< MinMaxranges
 
- Protected Attributes inherited from MLBase
bool trained
 
bool useScaling
 
DataType inputType
 
DataType outputType
 
UINT baseType
 
UINT numInputDimensions
 
UINT numOutputDimensions
 
UINT numTrainingIterationsToConverge
 
UINT minNumEpochs
 
UINT maxNumEpochs
 
UINT validationSetSize
 
Float learningRate
 
Float minChange
 
Float rootMeanSquaredTrainingError
 
Float totalSquaredTrainingError
 
Float validationSetAccuracy
 
bool useValidationSet
 
bool randomiseTrainingOrder
 
VectorFloat validationSetPrecision
 
VectorFloat validationSetRecall
 
Random random
 
std::vector< TrainingResult > trainingResults
 
TrainingResultsObserverManager trainingResultsObserverManager
 
TestResultsObserverManager testResultsObserverManager
 

Static Protected Attributes

static RegisterClustererModule< ClusterTreeregisterModule
 

Additional Inherited Members

- Public Types inherited from Tree
enum  TrainingMode { BEST_ITERATIVE_SPILT =0, BEST_RANDOM_SPLIT, NUM_TRAINING_MODES }
 
- Public Types inherited from Clusterer
typedef std::map< std::string, Clusterer *(*)() > StringClustererMap
 
- Public Types inherited from MLBase
enum  BaseTypes { BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER }
 
- Static Public Member Functions inherited from GRTBase
static std::string getGRTVersion (bool returnRevision=true)
 
static std::string getGRTRevison ()
 
- Static Public Member Functions inherited from Clusterer
static ClusterercreateInstanceFromString (std::string const &ClustererType)
 
static Vector< std::string > getRegisteredClusterers ()
 
- Static Protected Member Functions inherited from Clusterer
static StringClustererMapgetMap ()
 

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 40 of file ClusterTree.h.

Constructor & Destructor Documentation

ClusterTree::ClusterTree ( const UINT  numSplittingSteps = 100,
const UINT  minNumSamplesPerNode = 5,
const UINT  maxDepth = 10,
const bool  removeFeaturesAtEachSpilt = false,
const UINT  trainingMode = BEST_ITERATIVE_SPILT,
const bool  useScaling = false,
const Float  minRMSErrorPerNode = 0.01 
)

Default Constructor

Parameters
numSplittingStepssets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
minNumSamplesPerNodesets the minimum number of samples that are allowed per node, if the number of samples is below that, the node will become a leafNode. Default value = 5
maxDepthsets the maximum depth of the tree. Default value = 10
removeFeaturesAtEachSpiltsets if a feature is removed at each spilt so it can not be used again. Default value = false
trainingModesets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT
useScalingsets if the training and real-time data should be scaled between [0 1]. Default value = false
minRMSErrorPerNodesets the minimum RMS error that allowed per node, if the RMS error is below that, the node will become a leafNode. Default value = 0.01

Definition at line 31 of file ClusterTree.cpp.

ClusterTree::ClusterTree ( const ClusterTree rhs)

Defines the copy constructor.

Parameters
rhsthe instance from which all the data will be copied into this instance

Definition at line 48 of file ClusterTree.cpp.

ClusterTree::~ClusterTree ( void  )
virtual

Default Destructor

Definition at line 60 of file ClusterTree.cpp.

Member Function Documentation

bool ClusterTree::clear ( )
virtual

This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns
returns true if the module was cleared succesfully, false otherwise

Reimplemented from Tree.

Definition at line 207 of file ClusterTree.cpp.

bool ClusterTree::deepCopyFrom ( const Clusterer cluster)
virtual

This is required for the Gesture Recognition Pipeline for when the pipeline.setRegressifier(...) method is called. It clones the data from the Base Class Clusterer pointer into this instance

Parameters
clustera pointer to the Clusterer Base Class, this should be pointing to another ClusterTree instance
Returns
returns true if the clone was successfull, false otherwise

Reimplemented from Clusterer.

Definition at line 89 of file ClusterTree.cpp.

ClusterTreeNode * ClusterTree::deepCopyTree ( ) const
virtual

Deep copies the tree, returning a pointer to the new clusterer tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.

Returns
returns a pointer to a deep copy of the tree

Reimplemented from Tree.

Definition at line 372 of file ClusterTree.cpp.

Float ClusterTree::getMinRMSErrorPerNode ( ) const

Gets the minimum root mean squared error value that needs to be exceeded for the tree to continue growing at a specific node. If the RMS error is below this value then the node will be made into a leaf node.

Returns
returns the minimum RMS error per node

Definition at line 389 of file ClusterTree.cpp.

UINT ClusterTree::getPredictedClusterLabel ( ) const

Gets the predicted cluster label from the most recent call to predict( ... ). The cluster label will be zero if the model has been trained but no prediction has been run.

Returns
returns the most recent predicted cluster label

Definition at line 385 of file ClusterTree.cpp.

const ClusterTreeNode * ClusterTree::getTree ( ) const

Gets a pointer to the tree. NULL will be returned if the decision tree model has not be trained.

Returns
returns a const pointer to the tree

Definition at line 381 of file ClusterTree.cpp.

bool ClusterTree::loadModelFromFile ( std::fstream &  file)
virtual

This loads a trained model from a file. This overrides the loadModelFromFile function in the ML base class.

Parameters
filea reference to the file the model will be loaded from
Returns
returns true if the model was loaded successfully, false otherwise

Reimplemented from MLBase.

Definition at line 263 of file ClusterTree.cpp.

ClusterTree & ClusterTree::operator= ( const ClusterTree rhs)

Defines how the data from the rhs ClusterTree should be copied to this ClusterTree

Parameters
rhsanother instance of a ClusterTree
Returns
returns a pointer to this instance of the ClusterTree

Definition at line 65 of file ClusterTree.cpp.

bool ClusterTree::predict_ ( VectorFloat inputVector)
virtual

This predicts the class of the inputVector. This overrides the predict function in the ML base class.

Parameters
VectorFloatinputVector: the input Vector to predict
Returns
returns true if the prediction was performed, false otherwise

Reimplemented from MLBase.

Definition at line 174 of file ClusterTree.cpp.

bool ClusterTree::print ( ) const
virtual

Prints the tree to std::cout.

Returns
returns true if the model was printed

Reimplemented from Tree.

Definition at line 221 of file ClusterTree.cpp.

bool ClusterTree::saveModelToFile ( std::fstream &  file) const
virtual

This saves the trained model to a file. This overrides the saveModelToFile function in the ML base class.

Parameters
filea reference to the file the model will be saved to
Returns
returns true if the model was saved successfully, false otherwise

Reimplemented from MLBase.

Definition at line 227 of file ClusterTree.cpp.

bool ClusterTree::setMinRMSErrorPerNode ( const Float  minRMSErrorPerNode)

Sets the minimum RMS error that needs to be exceeded for the tree to continue growing at a specific node.

Returns
returns true if the parameter was updated

Definition at line 393 of file ClusterTree.cpp.

bool ClusterTree::train_ ( MatrixFloat trainingData)
virtual

This trains the ClusterTree model, using the labelled regression data. This overrides the train function in the ML base class.

Parameters
trainingDataa reference to the training data
Returns
returns true if the model was trained, false otherwise

Reimplemented from Clusterer.

Definition at line 119 of file ClusterTree.cpp.


The documentation for this class was generated from the following files: